Agriculture’s Multifunctionality, Sustainability, and Social Responsibility

We investigate the question whether the concept of corporate social responsibility (CSR) could be used to replace or complement those of multifunctionality and sustainability in the agri-food sector. It shows that the double role of citizens as tax payers and customers requests and allows us to directly link the problems of governance and stakeholder society in an intertemporal framework of total value maximisation and sustainable development. Thus, the concept of CSR provides a link between the views on agriculture’s multifunctionality and sustainability. Moreover, the fact that some actors in a vertical market, such as the agri-food chain, can exercise market power and absorb tax money and resource rents enforces the need of a broader perspective which involves concern about addresses the social responsibilities and performance of all actors along this value chain.agricultural policy, multifunctionality, sustainability, social responsibility, market power., D62, D63, Q01, Q18,

The non-permanence of optimal soil carbon sequestration

Carbon sequestration in agricultural soils is considered as an option of greenhouse gas mitigation in many countries. But, the economic potential is limited by the dynamic process of saturation and the opportunity cost of land use change. In addition, this article shows that permanence cannot, in general, be achieved in the strict sense of maintaining the soil carbon stock on an increased equilibrium level. Rather, a cyclical pattern with periodical release of sequestered carbon can be economically optimal from both the farmers’ and societal point of view.Agriculture, Climate policy, Carbon sequestration, Land use change, Economic analysis., Land Economics/Use, Q15, Q24, Q54.,

The Application of Robust Regression to a Production Function Comparison – the Example of Swiss Corn

The adequate representation of crop response functions is crucial for agri-environmental modeling and analysis. So far, the evaluation of such functions focused on the comparison of different functional forms. The perspective is expanded in this article by considering an alternative regression method. This is motivated by the fact that exceptional crop yield observations (outliers) can cause misleading results if least squares regression is applied. We show that such outliers are adequately treated if robust regression is used instead. The example of simulated Swiss corn yields shows that the use of robust regression narrows the range of optimal input levels across different functional forms and reduces potential costs of misspecification.production function estimation, production function comparison, robust regression, crop response

Sustainable development with stock pollution

Optimal pollution control is an important challenge for sustainable development with three distinct cases. First, the situation where nature's assimilative capacity is completely destroyed involves normative problems that require further research. Second, environmental restoration with initial pollution above the steady-state stock requires an economy to initially allocate a relatively high share of its resources to cleaning-up activities. In return, this generally results in an intertemporally efficient development path that is both environmentally and economically sustainable. Third, optimal trajectories in situations with initial stocks of pollution below the long-term optimum generally imply an increase in pollution and a decline of optimal consumption. In this case, the investment of the environmental rents accruing from nature's assimilative capacity into man-made capital is required in analogy to the famous Hartwick rule to maintain a constant flow of instantaneous welfare. This would facilitate growth in consumption sufficient to compensate for the rising disutility of pollutio

Irrigation as adaptation strategy to climate change—a biophysical and economic appraisal for Swiss maize production

The impact of climate change on Swiss maize production is assessed using an approach that integrates a biophysical and an economic model. Simple adaptation options such as shifts in sowing dates and adjustments of production intensity are considered. In addition, irrigation is evaluated as an adaptation strategy. It shows that the impact of climate change on yield levels is small but yield variability increases in rainfed production. Even though the adoption of irrigation leads to higher and less variable maize yields in the future, economic benefits of this adoption decision are expected to be rather small. Thus, no shift from the currently used rainfed system to irrigated production is expected in the future. Moreover, we find that changes in institutional and market conditions rather than changes in climatic conditions will influence the development of the Swiss maize production and the adoption of irrigation in the futur

A Computational Methodology to Screen Activities of Enzyme Variants

We present a fast computational method to efficiently screen enzyme activity. In the presented method, the effect of mutations on the barrier height of an enzyme-catalysed reaction can be computed within 24 hours on roughly 10 processors. The methodology is based on the PM6 and MOZYME methods as implemented in MOPAC2009, and is tested on the first step of the amide hydrolysis reaction catalyzed by Candida Antarctica lipase B (CalB) enzyme. The barrier heights are estimated using adiabatic mapping and are shown to give barrier heights to within 3kcal/mol of B3LYP/6-31G(d)//RHF/3-21G results for a small model system. Relatively strict convergence criteria (0.5kcal/(mol{\AA})), long NDDO cutoff distances within the MOZYME method (15{\AA}) and single point evaluations using conventional PM6 are needed for reliable results. The generation of mutant structure and subsequent setup of the semiempirical calculations are automated so that the effect on barrier heights can be estimated for hundreds of mutants in a matter of weeks using high performance computing

The Application of Robust Regression to a Production Function Comparison – the Example of Swiss Corn

The adequate representation of crop response functions is crucial for agricultural modeling and analysis. So far, the evaluation of such functions focused on the comparison of different functional forms. In this article, the perspective is expanded by also considering an alternative regression method. This is motivated by the fact that extreme climatic events can result in crop yield observations that cause misleading results if Least Squares regression is applied. We show that such outliers are adequately treated if and only if robust regression or robust diagnostics are applied. The example of simulated Swiss corn yields shows that the application of robust instead of Least Squares regression causes reasonable shifts in coefficient estimates and their level of significance, and results in higher levels of goodness of fit. Furthermore, the costs of misspecification decrease remarkably if optimal input recommendations are based on results of robust regression. We therefore recommend the application of the latter instead of Least Squares regression for agricultural and environmental production function estimation

The Application of Robust Regression to a Production Function Comparison – the Example of Swiss Corn

The adequate representation of crop response functions is crucial for agri-environmental modeling and analysis. So far, the evaluation of such functions focused on the comparison of different functional forms. The perspective is expanded in this article by considering an alternative regression method. This is motivated by the fact that exceptional crop yield observations (outliers) can cause misleading results if least squares regression is applied. We show that such outliers are adequately treated if robust regression is used instead. The example of simulated Swiss corn yields shows that the use of robust regression narrows the range of optimal input levels across different functional forms and reduces potential costs of misspecification

The Application of Robust Regression to a Production Function Comparison – the Example of Swiss Corn

The adequate representation of crop response functions is crucial for agricultural modeling and analysis. So far, the evaluation of such functions focused on the comparison of different functional forms. In this article, the perspective is expanded by also considering an alternative regression method. This is motivated by the fact that extreme climatic events can result in crop yield observations that cause misleading results if Least Squares regression is applied. We show that such outliers are adequately treated if and only if robust regression or robust diagnostics are applied. The example of simulated Swiss corn yields shows that the application of robust instead of Least Squares regression causes reasonable shifts in coefficient estimates and their level of significance, and results in higher levels of goodness of fit. Furthermore, the costs of misspecification decrease remarkably if optimal input recommendations are based on results of robust regression. We therefore recommend the application of the latter instead of Least Squares regression for agricultural and environmental production function estimation